Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing

Pratiwi, Winda; Sofwan, Aghus; Setiawan, Iwan

doi:10.11591/ijai.v10.i3.pp545-552

Cited by 7 publications

(8 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computer-based decision-making process in this study is additionally aided by a user interface (UI) [18]. In the selection process, Figure 4 shows the user interface for determining whether a student is accepted or denied (PMB).…”

Section: Results and Analysismentioning

confidence: 99%

Fuzzy Decision Support System for ABC University Student Admission Selection

Yudono¹,

Faris²,

Wibowo³

et al. 2022

Proceedings of the International Conference on Economics, Management and Accounting (ICEMAC 2021)

View full text Add to dashboard Cite

New Student Admission (PMB) is a pattern for selecting prospective students. Every year, every University conducts PMB selection, and new student admissions are divided into numerous tracks: the Independent Path. The Basic Competency Exam, TOEFL Prediction, and interviews are commonly used in this selection process. So far, the registrant data has been manually sorted in the selection process. A system that can help determine prospective new students will be required since many student data will be picked for PMB. Because of the system's assistance, universities can make the selection process more effective. The method used in this study to implement a PMB decision system is Fuzzy Mamdani. The method in this study has a sequence of stages: first doing fuzzification, second using inference evaluation rules that have been determined, and finally, defuzzification to get the final results of the calculations. This system was tested by comparing the system's results to the actual results obtained according to the design. The system testing results received a 96 percent approval rating. As a result, the University's new student admissions system can be used to help streamline the formerly manual selection process.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Fuzzy Decision Support System for ABC University Student Admission Selection

Yudono¹,

Faris²,

Wibowo³

et al. 2022

Proceedings of the International Conference on Economics, Management and Accounting (ICEMAC 2021)

View full text Add to dashboard Cite

show abstract

“…Fuzzy set theory provides a framework for dealing with classes of objects in which the boundaries are not precisely defined. Instead of strict, absolute, binary membership (either true or false) [ 7 ], fuzzy sets allow for degrees of membership, acknowledging the gradual transition between the two aspects [ 10 ].…”

Section: The Designed Fuzzy System For Ship Deck Landingmentioning

confidence: 99%

“…The authors in [ 10 ] present a proposed fuzzy logic framework for intelligent aircraft landing decision systems. Various factors influencing the required aircraft landing decisions are incorporated in this approach, encompassing wind velocity, wind direction, visibility, and pilot experience.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Tsitses,

Zacharia,

Xidias

et al. 2024

Sensors

View full text Add to dashboard Cite

This paper introduces a fuzzy logic-based autonomous ship deck landing system for fixed-wing unmanned aerial vehicles (UAVs). The ship is assumed to maintain a constant course and speed. The aim of this fuzzy logic landing model is to simplify the task of landing UAVs on moving ships in challenging maritime conditions, relieving operators from this demanding task. The designed UAV ship deck landing model is based on a fuzzy logic system (FLS), which comprises three interconnected subsystems (speed, lateral motion, and altitude components). Each subsystem consists of three inputs and one output incorporating various fuzzy rules to account for external factors during ship deck landings. Specifically, the FLS receives five inputs: the range from the deck, the relative wind direction and speed, the airspeed, and the UAV’s flight altitude. The FLS outputs provide data on the speed of the UAV relative to the ship’s velocity, the bank angle (BA), and the angle of descent (AOD) of the UAV. The performance of the designed intelligent ship deck landing system was evaluated using the standard configuration of MATLAB Fuzzy Toolbox.

show abstract

“…Therefore, clinical inertia may result when there is much information, and a physician analyzes it cognitively. Fuzzy expert systems (FESs) provide an adequate methodology to design robust systems that can offer satisfactory performance when confronted with the uncertainty, noise, and inaccuracy attributed to real-world environments and applications [11]. The Mamdani method is characterized as a simple fuzzy control system that accepts data input and translates it to linguistic terms (fuzzification).…”

Section:  Issn: 2252-8938mentioning

confidence: 99%

Intelligent fuzzy system to assess the risk of type 2 diabetes and diagnosis in marginalized regions

Grande-Ramírez,

Meza-Palacios,

Aguilar-Lasserre

et al. 2024

IJ-AI

View full text Add to dashboard Cite

Diabetes is one of the leading causes of death in the world and continues to rise. Type 2 diabetes mellitus is a life-threatening chronic degenerative disease if not appropriately controlled; risk factors and ineffective diagnosis continue to increase its prevalence. This study proposes an intelligent fuzzy system to make a diagnosis and predict the risk of developing type 2 diabetes mellitus. The system consists of two models; the R-T2DM model estimates if a person is at risk of developing type 2 diabetes mellitus. The DT2DM model is based on two systems: the symptomatology system estimates the level of symptoms the patient has, and the diagnosis system diagnoses type 2 diabetes mellitus. The results of this research were compared with those estimated by the team of doctors, and it was observed that the R-T2DM model obtained a success rate of 90.3%. The D-T2DM model got a success rate of 88.3% for the symptomatology system and 95.5% for the diagnosis system. The model developed in this study is focused on being applied in economically marginalized geographic areas of Mexico to improve the patient's quality of life.

show abstract

Implementation of fuzzy logic method for automation of decision making of Boeing aircraft landing

Cited by 7 publications

References 20 publications

Fuzzy Decision Support System for ABC University Student Admission Selection

Fuzzy Decision Support System for ABC University Student Admission Selection

A Fuzzy-Based System for Autonomous Unmanned Aerial Vehicle Ship Deck Landing

Intelligent fuzzy system to assess the risk of type 2 diabetes and diagnosis in marginalized regions

Contact Info

Product

Resources

About